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12
README.md
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@ -22,9 +22,9 @@ desktop/cloud, web and IoT devices.
## ML solutions in MediaPipe ## ML solutions in MediaPipe
Face Detection | Face Mesh | Hand | Hair Segmentation Face Detection | Face Mesh | Hands | Hair Segmentation
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---------------: :----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :---------------:
[![face_detection](docs/images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](docs/images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![hand](docs/images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hand) | [![hair_segmentation](docs/images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) [![face_detection](docs/images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](docs/images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![hand](docs/images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![hair_segmentation](docs/images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation)
Object Detection | Box Tracking | Objectron | KNIFT Object Detection | Box Tracking | Objectron | KNIFT
:----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---: :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
@ -37,7 +37,7 @@ Object Detection
:---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---: :---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---:
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅ [Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | | [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | |
[Hand](https://google.github.io/mediapipe/solutions/hand) | ✅ | ✅ | ✅ | ✅ | [Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ | [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅ [Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | [Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | |
@ -63,8 +63,8 @@ never leaves your device.
![visualizer_runner](docs/images/visualizer_runner.png) ![visualizer_runner](docs/images/visualizer_runner.png)
* [MediaPipe Face Detection](https://viz.mediapipe.dev/demo/face_detection) * [MediaPipe Face Detection](https://viz.mediapipe.dev/demo/face_detection)
* [MediaPipe Hand](https://viz.mediapipe.dev/demo/hand_tracking) * [MediaPipe Hands](https://viz.mediapipe.dev/demo/hand_tracking)
* [MediaPipe Hand (palm/hand detection only)](https://viz.mediapipe.dev/demo/hand_detection) * [MediaPipe Hands (palm/hand detection only)](https://viz.mediapipe.dev/demo/hand_detection)
* [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation) * [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation)
## Getting started ## Getting started

140
build_android_examples.sh Normal file
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@ -0,0 +1,140 @@
#!/bin/bash
# Copyright 2020 The MediaPipe Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =========================================================================
#
# Script to build all MediaPipe Android example apps.
#
# To build all apps and store them in out_dir, and install them:
# $ ./build_android_examples.sh -d out_dir
# Omitting -d and the associated directory saves all generated APKs in the
# current directory.
# $ ./build_android_examples.sh -d out_dir --nostrip
# Same as above except that the symnbols are not stripped.
#
# To install the apps already stored in out_dir (after building them with the
# usages above):
# $ ./build_android_examples.sh -d out_dir -i
# Omitting -d and the associated directory assumes the apps are in the
# current directory.
set -e
function switch_to_opencv_3() {
echo "Switching to OpenCV 3"
sed -i -e 's:4.0.1/opencv-4.0.1:3.4.3/opencv-3.4.3:g' WORKSPACE
sed -i -e 's:libopencv_java4:libopencv_java3:g' third_party/opencv_android.BUILD
}
function switch_to_opencv_4() {
echo "Switching to OpenCV 4"
sed -i -e 's:3.4.3/opencv-3.4.3:4.0.1/opencv-4.0.1:g' WORKSPACE
sed -i -e 's:libopencv_java3:libopencv_java4:g' third_party/opencv_android.BUILD
}
out_dir="."
strip=true
install_only=false
app_dir="mediapipe/examples/android/src/java/com/google/mediapipe/apps"
bin_dir="bazel-bin"
declare -a default_bazel_flags=(build -c opt --config=android_arm64)
while [[ -n $1 ]]; do
case $1 in
-d)
shift
out_dir=$1
;;
--nostrip)
strip=false
;;
-i)
install_only=true
;;
*)
echo "Unsupported input argument $1."
exit 1
;;
esac
shift
done
echo "app_dir: $app_dir"
echo "out_dir: $out_dir"
echo "strip: $strip"
declare -a apks=()
declare -a bazel_flags
switch_to_opencv_3
apps="${app_dir}/*"
for app in ${apps}; do
if [[ -d "${app}" ]]; then
app_name=${app##*/}
if [[ ${app_name} == "basic" ]]; then
target_name="helloworld"
else
target_name=${app_name}
fi
target="${app}:${target_name}"
bin="${bin_dir}/${app}/${target_name}.apk"
apk="${out_dir}/${target_name}.apk"
echo "=== Target: ${target}"
if [[ $install_only == false ]]; then
bazel_flags=("${default_bazel_flags[@]}")
bazel_flags+=(${target})
if [[ $strip == true ]]; then
bazel_flags+=(--linkopt=-s)
fi
if [[ ${app_name} == "templatematchingcpu" ]]; then
switch_to_opencv_4
fi
bazel "${bazel_flags[@]}"
cp -f "${bin}" "${apk}"
if [[ ${app_name} == "templatematchingcpu" ]]; then
switch_to_opencv_3
fi
fi
if [[ ${app_name} == "objectdetection3d" ]]; then
orig_apk=${apk}
apk="${out_dir}/${target_name}_shoes.apk"
cp -f "${orig_apk}" "${apk}"
apks+=(${apk})
apk="${out_dir}/${target_name}_chairs.apk"
if [[ $install_only == false ]]; then
bazel_flags+=(--define chair=true)
bazel "${bazel_flags[@]}"
cp -f "${bin}" "${apk}"
fi
fi
apks+=(${apk})
fi
done
echo
echo "Connect your device via adb to install the apps."
read -p "Press 'a' to abort, or press any other key to continue ..." -n 1 -r
echo
if [[ ! $REPLY =~ ^[Aa]$ ]]; then
for apk in "${apks[@]}"; do
echo "=== Installing $apk"
adb install -r "${apk}"
done
fi

24
docs/getting_started/building_examples.md
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@ -23,7 +23,7 @@ nav_order: 2
MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
below for Android Studio setup). However, if you prefer using MediaPipe without below for Android Studio setup). However, if you prefer using MediaPipe without
Android Studio, please run Android Studio, please run
[`setup_android_sdk_and_ndk.sh`](https://github.com/google/mediapipe/tree/master/setup_android_sdk_and_ndk.sh) [`setup_android_sdk_and_ndk.sh`](https://github.com/google/mediapipe/blob/master/setup_android_sdk_and_ndk.sh)
to download and setup Android SDK and NDK before building any Android example to download and setup Android SDK and NDK before building any Android example
apps. apps.
@ -39,7 +39,7 @@ In order to use MediaPipe on earlier Android versions, MediaPipe needs to switch
to a lower Android API level. You can achieve this by specifying `api_level = to a lower Android API level. You can achieve this by specifying `api_level =
$YOUR_INTENDED_API_LEVEL` in android_ndk_repository() and/or $YOUR_INTENDED_API_LEVEL` in android_ndk_repository() and/or
android_sdk_repository() in the android_sdk_repository() in the
[`WORKSPACE`](https://github.com/google/mediapipe/tree/master/WORKSPACE) file. [`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE) file.
Please verify all the necessary packages are installed. Please verify all the necessary packages are installed.
@ -51,9 +51,13 @@ Please verify all the necessary packages are installed.
### Option 1: Build with Bazel in Command Line ### Option 1: Build with Bazel in Command Line
Tip: You can run this
[script](https://github.com/google/mediapipe/blob/master/build_android_examples.sh)
to build (and install) all MediaPipe Android example apps.
1. To build an Android example app, build against the corresponding 1. To build an Android example app, build against the corresponding
`android_binary` build target. For instance, for `android_binary` build target. For instance, for
[MediaPipe Hand](../solutions/hand.md) the target is `handtrackinggpu` in [MediaPipe Hands](../solutions/hands.md) the target is `handtrackinggpu` in
the the
[BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/BUILD) [BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/BUILD)
file: file:
@ -65,7 +69,7 @@ Please verify all the necessary packages are installed.
bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu
``` ```
1. Install it on a device with: 2. Install it on a device with:
```bash ```bash
adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/handtrackinggpu.apk adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/handtrackinggpu.apk
@ -149,8 +153,8 @@ app:
Note: Even after doing step 4, if you still see the error: `"no such package Note: Even after doing step 4, if you still see the error: `"no such package
'@androidsdk//': Either the path attribute of android_sdk_repository or the '@androidsdk//': Either the path attribute of android_sdk_repository or the
ANDROID_HOME environment variable must be set."`, please modify the ANDROID_HOME environment variable must be set."`, please modify the
[`WORKSPACE`](https://github.com/google/mediapipe/tree/master/WORKSPACE) file to point to your [`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE)
SDK and NDK library locations, as below: file to point to your SDK and NDK library locations, as below:
``` ```
android_sdk_repository( android_sdk_repository(
@ -229,12 +233,12 @@ app:
1. Modify the `bundle_id` field of the app's `ios_application` build target to 1. Modify the `bundle_id` field of the app's `ios_application` build target to
use your own identifier. For instance, for use your own identifier. For instance, for
[MediaPipe Hand](../solutions/hand.md), the `bundle_id` is in the [MediaPipe Hands](../solutions/hands.md), the `bundle_id` is in the
`HandTrackingGpuApp` target in the `HandTrackingGpuApp` target in the
[BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handtrackinggpu/BUILD) [BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handtrackinggpu/BUILD)
file. file.
2. Again using [MediaPipe Hand](../solutions/hand.md) for example, run: 2. Again using [MediaPipe Hands](../solutions/hands.md) for example, run:
```bash ```bash
bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp
@ -298,7 +302,7 @@ the previous section.
### Option 1: Running on CPU ### Option 1: Running on CPU
1. To build, for example, [MediaPipe Hand](../solutions/hand.md), run: 1. To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
```bash ```bash
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu
@ -319,7 +323,7 @@ the previous section.
Note: This currently works only on Linux, and please first follow Note: This currently works only on Linux, and please first follow
[OpenGL ES Setup on Linux Desktop](./gpu_support.md#opengl-es-setup-on-linux-desktop). [OpenGL ES Setup on Linux Desktop](./gpu_support.md#opengl-es-setup-on-linux-desktop).
1. To build, for example, [MediaPipe Hand](../solutions/hand.md), run: 1. To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
```bash ```bash
bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 \ bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 \

6
docs/getting_started/install.md
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@ -140,6 +140,8 @@ apps, see these [instructions](./building_examples.md#ios).
## Installing on CentOS ## Installing on CentOS
**Disclaimer**: Running MediaPipe on CentOS is experimental.
1. Checkout MediaPipe repository. 1. Checkout MediaPipe repository.
```bash ```bash
@ -668,8 +670,8 @@ This will use a Docker image that will isolate mediapipe's installation from the
docker run -i -t mediapipe:latest docker run -i -t mediapipe:latest
``` --> ``` -->
[`WORKSPACE`]: https://github.com/google/mediapipe/tree/master/WORKSPACE [`WORKSPACE`]: https://github.com/google/mediapipe/blob/master/WORKSPACE
[`opencv_linux.BUILD`]: https://github.com/google/mediapipe/tree/master/third_party/opencv_linux.BUILD [`opencv_linux.BUILD`]: https://github.com/google/mediapipe/tree/master/third_party/opencv_linux.BUILD
[`opencv_macos.BUILD`]: https://github.com/google/mediapipe/tree/master/third_party/opencv_macos.BUILD [`opencv_macos.BUILD`]: https://github.com/google/mediapipe/tree/master/third_party/opencv_macos.BUILD
[`ffmpeg_macos.BUILD`]:https://github.com/google/mediapipe/tree/master/third_party/ffmpeg_macos.BUILD [`ffmpeg_macos.BUILD`]:https://github.com/google/mediapipe/tree/master/third_party/ffmpeg_macos.BUILD
[`setup_opencv.sh`]: https://github.com/google/mediapipe/tree/master/setup_opencv.sh [`setup_opencv.sh`]: https://github.com/google/mediapipe/blob/master/setup_opencv.sh

12
docs/index.md
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@ -22,9 +22,9 @@ desktop/cloud, web and IoT devices.
## ML solutions in MediaPipe ## ML solutions in MediaPipe
Face Detection | Face Mesh | Hand | Hair Segmentation Face Detection | Face Mesh | Hands | Hair Segmentation
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---------------: :----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :---------------:
[![face_detection](images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![hand](images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hand) | [![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) [![face_detection](images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![hand](images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation)
Object Detection | Box Tracking | Objectron | KNIFT Object Detection | Box Tracking | Objectron | KNIFT
:----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---: :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
@ -37,7 +37,7 @@ Object Detection
:---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---: :---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---:
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅ [Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | | [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | |
[Hand](https://google.github.io/mediapipe/solutions/hand) | ✅ | ✅ | ✅ | ✅ | [Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ | [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅ [Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | [Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | |
@ -63,8 +63,8 @@ never leaves your device.
![visualizer_runner](images/visualizer_runner.png) ![visualizer_runner](images/visualizer_runner.png)
* [MediaPipe Face Detection](https://viz.mediapipe.dev/demo/face_detection) * [MediaPipe Face Detection](https://viz.mediapipe.dev/demo/face_detection)
* [MediaPipe Hand](https://viz.mediapipe.dev/demo/hand_tracking) * [MediaPipe Hands](https://viz.mediapipe.dev/demo/hand_tracking)
* [MediaPipe Hand (palm/hand detection only)](https://viz.mediapipe.dev/demo/hand_detection) * [MediaPipe Hands (palm/hand detection only)](https://viz.mediapipe.dev/demo/hand_detection)
* [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation) * [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation)
## Getting started ## Getting started

2
docs/solutions/face_mesh.md
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@ -44,7 +44,7 @@ prediction accuracy. In addition, in our pipeline the crops can also be
generated based on the face landmarks identified in the previous frame, and only generated based on the face landmarks identified in the previous frame, and only
when the landmark model could no longer identify face presence is the face when the landmark model could no longer identify face presence is the face
detector invoked to relocalize the face. This strategy is similar to that detector invoked to relocalize the face. This strategy is similar to that
employed in our [MediaPipe Hand](./hand.md) solution, which uses a palm detector employed in our [MediaPipe Hands](./hands.md) solution, which uses a palm detector
together with a hand landmark model. together with a hand landmark model.
The pipeline is implemented as a MediaPipe The pipeline is implemented as a MediaPipe

6
docs/solutions/hand.md → docs/solutions/hands.md
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@ -5,7 +5,7 @@ parent: Solutions
nav_order: 3 nav_order: 3
--- ---
# MediaPipe Hand # MediaPipe Hands
{: .no_toc } {: .no_toc }
1. TOC 1. TOC
@ -23,7 +23,7 @@ naturally to people, robust real-time hand perception is a decidedly challenging
computer vision task, as hands often occlude themselves or each other (e.g. computer vision task, as hands often occlude themselves or each other (e.g.
finger/palm occlusions and hand shakes) and lack high contrast patterns. finger/palm occlusions and hand shakes) and lack high contrast patterns.
MediaPipe Hand is a high-fidelity hand and finger tracking solution. It employs MediaPipe Hands is a high-fidelity hand and finger tracking solution. It employs
machine learning (ML) to infer 21 3D landmarks of a hand from just a single machine learning (ML) to infer 21 3D landmarks of a hand from just a single
frame. Whereas current state-of-the-art approaches rely primarily on powerful frame. Whereas current state-of-the-art approaches rely primarily on powerful
desktop environments for inference, our method achieves real-time performance on desktop environments for inference, our method achieves real-time performance on
@ -38,7 +38,7 @@ and new research avenues.
## ML Pipeline ## ML Pipeline
MediaPipe Hand utilizes an ML pipeline consisting of multiple models working MediaPipe Hands utilizes an ML pipeline consisting of multiple models working
together: A palm detection model that operates on the full image and returns an together: A palm detection model that operates on the full image and returns an
oriented hand bounding box. A hand landmark model that operates on the cropped oriented hand bounding box. A hand landmark model that operates on the cropped
image region defined by the palm detector and returns high-fidelity 3D hand image region defined by the palm detector and returns high-fidelity 3D hand

2
docs/solutions/solutions.md
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@ -20,7 +20,7 @@ has_toc: false
:---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---: :---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---:
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅ [Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | | [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | |
[Hand](https://google.github.io/mediapipe/solutions/hand) | ✅ | ✅ | ✅ | ✅ | [Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ | [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅ [Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | [Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | |

2
docs/tools/visualizer.md
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@ -82,7 +82,7 @@ used. Clicking on a subgraph will navigate to the corresponding tab which holds
the subgraph's definition. the subgraph's definition.
For instance, there are two graphs involved in For instance, there are two graphs involved in
[MediaPipe Hand](../solutions/hand.md): the main graph [MediaPipe Hands](../solutions/hands.md): the main graph
([source pbtxt file](https://github.com/google/mediapipe/blob/master/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt)) ([source pbtxt file](https://github.com/google/mediapipe/blob/master/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt))
and its associated subgraph and its associated subgraph
([source pbtxt file](https://github.com/google/mediapipe/blob/master/mediapipe/graphs/hand_tracking/subgraphs/hand_detection_gpu.pbtxt)). ([source pbtxt file](https://github.com/google/mediapipe/blob/master/mediapipe/graphs/hand_tracking/subgraphs/hand_detection_gpu.pbtxt)).

2
mediapipe/MediaPipe.tulsiproj/Configs/MediaPipe.tulsigen
View File

@ -7,6 +7,7 @@
"mediapipe/examples/ios/edgedetectiongpu/BUILD", "mediapipe/examples/ios/edgedetectiongpu/BUILD",
"mediapipe/examples/ios/facedetectioncpu/BUILD", "mediapipe/examples/ios/facedetectioncpu/BUILD",
"mediapipe/examples/ios/facedetectiongpu/BUILD", "mediapipe/examples/ios/facedetectiongpu/BUILD",
"mediapipe/examples/ios/facemeshgpu/BUILD",
"mediapipe/examples/ios/handdetectiongpu/BUILD", "mediapipe/examples/ios/handdetectiongpu/BUILD",
"mediapipe/examples/ios/handtrackinggpu/BUILD", "mediapipe/examples/ios/handtrackinggpu/BUILD",
"mediapipe/examples/ios/multihandtrackinggpu/BUILD", "mediapipe/examples/ios/multihandtrackinggpu/BUILD",
@ -17,6 +18,7 @@
"//mediapipe/examples/ios/edgedetectiongpu:EdgeDetectionGpuApp", "//mediapipe/examples/ios/edgedetectiongpu:EdgeDetectionGpuApp",
"//mediapipe/examples/ios/facedetectioncpu:FaceDetectionCpuApp", "//mediapipe/examples/ios/facedetectioncpu:FaceDetectionCpuApp",
"//mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp", "//mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp",
"//mediapipe/examples/ios/facemeshgpu:FaceMeshGpuApp",
"//mediapipe/examples/ios/handdetectiongpu:HandDetectionGpuApp", "//mediapipe/examples/ios/handdetectiongpu:HandDetectionGpuApp",
"//mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp", "//mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp",
"//mediapipe/examples/ios/multihandtrackinggpu:MultiHandTrackingGpuApp", "//mediapipe/examples/ios/multihandtrackinggpu:MultiHandTrackingGpuApp",

1
mediapipe/MediaPipe.tulsiproj/project.tulsiconf
View File

@ -14,6 +14,7 @@
"mediapipe/examples/ios/edgedetectiongpu", "mediapipe/examples/ios/edgedetectiongpu",
"mediapipe/examples/ios/facedetectioncpu", "mediapipe/examples/ios/facedetectioncpu",
"mediapipe/examples/ios/facedetectiongpu", "mediapipe/examples/ios/facedetectiongpu",
"mediapipe/examples/ios/facemeshgpu",
"mediapipe/examples/ios/handdetectiongpu", "mediapipe/examples/ios/handdetectiongpu",
"mediapipe/examples/ios/handtrackinggpu", "mediapipe/examples/ios/handtrackinggpu",
"mediapipe/examples/ios/multihandtrackinggpu", "mediapipe/examples/ios/multihandtrackinggpu",

94
mediapipe/calculators/tflite/tflite_inference_calculator.cc
View File

@ -354,10 +354,7 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
#endif // !MEDIAPIPE_DISABLE_GPU #endif // !MEDIAPIPE_DISABLE_GPU
} }
const auto& calculator_opts =
cc->Options<mediapipe::TfLiteInferenceCalculatorOptions>();
use_advanced_gpu_api_ = false; use_advanced_gpu_api_ = false;
if (use_advanced_gpu_api_ && !(gpu_input_ && gpu_output_)) { if (use_advanced_gpu_api_ && !(gpu_input_ && gpu_output_)) {
LOG(WARNING) LOG(WARNING)
<< "Cannot use advanced GPU APIs, both inputs and outputs must " << "Cannot use advanced GPU APIs, both inputs and outputs must "
@ -393,29 +390,15 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
return ::mediapipe::OkStatus(); return ::mediapipe::OkStatus();
} }
::mediapipe::Status TfLiteInferenceCalculator::InitTFLiteGPURunner() {
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// Create and bind OpenGL buffers for outputs.
// These buffers are created onve and later their ids are jut passed to the
// calculator outputs.
gpu_data_out_.resize(tflite_gpu_runner_->outputs_size());
for (int i = 0; i < tflite_gpu_runner_->outputs_size(); ++i) {
gpu_data_out_[i] = absl::make_unique<GPUData>();
ASSIGN_OR_RETURN(gpu_data_out_[i]->elements,
tflite_gpu_runner_->GetOutputElements(i));
// Create and bind input buffer.
RET_CHECK_CALL(::tflite::gpu::gl::CreateReadWriteShaderStorageBuffer<float>(
gpu_data_out_[i]->elements, &gpu_data_out_[i]->buffer));
}
RET_CHECK_CALL(tflite_gpu_runner_->Build());
#endif
return ::mediapipe::OkStatus();
}
::mediapipe::Status TfLiteInferenceCalculator::Process(CalculatorContext* cc) { ::mediapipe::Status TfLiteInferenceCalculator::Process(CalculatorContext* cc) {
// 0. Declare outputs
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE) || defined(MEDIAPIPE_IOS)
auto output_tensors_gpu = absl::make_unique<std::vector<GpuTensor>>();
#endif
auto output_tensors_cpu = absl::make_unique<std::vector<TfLiteTensor>>();
// 1. Receive pre-processed tensor inputs. // 1. Receive pre-processed tensor inputs.
if (use_advanced_gpu_api_) { if (use_advanced_gpu_api_ && gpu_output_) {
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE) #if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
if (cc->Inputs().Tag(kTensorsGpuTag).IsEmpty()) { if (cc->Inputs().Tag(kTensorsGpuTag).IsEmpty()) {
return ::mediapipe::OkStatus(); return ::mediapipe::OkStatus();
@ -424,14 +407,19 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
cc->Inputs().Tag(kTensorsGpuTag).Get<std::vector<GpuTensor>>(); cc->Inputs().Tag(kTensorsGpuTag).Get<std::vector<GpuTensor>>();
RET_CHECK(!input_tensors.empty()); RET_CHECK(!input_tensors.empty());
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext( MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext(
[this, &input_tensors]() -> ::mediapipe::Status { [this, &input_tensors, &output_tensors_gpu]() -> ::mediapipe::Status {
for (int i = 0; i < input_tensors.size(); ++i) { for (int i = 0; i < input_tensors.size(); ++i) {
MP_RETURN_IF_ERROR(tflite_gpu_runner_->BindSSBOToInputTensor( MP_RETURN_IF_ERROR(tflite_gpu_runner_->BindSSBOToInputTensor(
input_tensors[i].id(), i)); input_tensors[i].id(), i));
} }
// Allocate output tensor.
output_tensors_gpu->resize(gpu_data_out_.size());
for (int i = 0; i < gpu_data_out_.size(); ++i) { for (int i = 0; i < gpu_data_out_.size(); ++i) {
MP_RETURN_IF_ERROR(tflite_gpu_runner_->BindSSBOToOutputTensor( GpuTensor& tensor = output_tensors_gpu->at(i);
gpu_data_out_[i]->buffer.id(), i)); RET_CHECK_CALL(CreateReadWriteShaderStorageBuffer<float>(
gpu_data_out_[i]->elements, &tensor));
MP_RETURN_IF_ERROR(
tflite_gpu_runner_->BindSSBOToOutputTensor(tensor.id(), i));
} }
return ::mediapipe::OkStatus(); return ::mediapipe::OkStatus();
})); }));
@ -532,24 +520,19 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
// 3. Output processed tensors. // 3. Output processed tensors.
if (use_advanced_gpu_api_) { if (use_advanced_gpu_api_) {
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE) #if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
auto output_tensors = absl::make_unique<std::vector<GpuTensor>>();
output_tensors->resize(gpu_data_out_.size());
for (int i = 0; i < gpu_data_out_.size(); ++i) {
output_tensors->at(i) = gpu_data_out_[i]->buffer.MakeRef();
}
cc->Outputs() cc->Outputs()
.Tag(kTensorsGpuTag) .Tag(kTensorsGpuTag)
.Add(output_tensors.release(), cc->InputTimestamp()); .Add(output_tensors_gpu.release(), cc->InputTimestamp());
#endif #endif
} else if (gpu_output_) { } else if (gpu_output_) {
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE) #if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// Output result tensors (GPU). // Output result tensors (GPU).
auto output_tensors = absl::make_unique<std::vector<GpuTensor>>();
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext( MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext(
[this, &output_tensors]() -> ::mediapipe::Status { [this, &output_tensors_gpu]() -> ::mediapipe::Status {
output_tensors->resize(gpu_data_out_.size()); output_tensors_gpu->resize(gpu_data_out_.size());
for (int i = 0; i < gpu_data_out_.size(); ++i) { for (int i = 0; i < gpu_data_out_.size(); ++i) {
GpuTensor& tensor = output_tensors->at(i); GpuTensor& tensor = output_tensors_gpu->at(i);
// Allocate output tensor.
RET_CHECK_CALL(CreateReadWriteShaderStorageBuffer<float>( RET_CHECK_CALL(CreateReadWriteShaderStorageBuffer<float>(
gpu_data_out_[i]->elements, &tensor)); gpu_data_out_[i]->elements, &tensor));
RET_CHECK_CALL(CopyBuffer(gpu_data_out_[i]->buffer, tensor)); RET_CHECK_CALL(CopyBuffer(gpu_data_out_[i]->buffer, tensor));
@ -558,45 +541,44 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
})); }));
cc->Outputs() cc->Outputs()
.Tag(kTensorsGpuTag) .Tag(kTensorsGpuTag)
.Add(output_tensors.release(), cc->InputTimestamp()); .Add(output_tensors_gpu.release(), cc->InputTimestamp());
#elif defined(MEDIAPIPE_IOS) #elif defined(MEDIAPIPE_IOS)
// Output result tensors (GPU). // Output result tensors (GPU).
auto output_tensors = absl::make_unique<std::vector<GpuTensor>>(); output_tensors_gpu->resize(gpu_data_out_.size());
output_tensors->resize(gpu_data_out_.size());
id<MTLDevice> device = gpu_helper_.mtlDevice; id<MTLDevice> device = gpu_helper_.mtlDevice;
id<MTLCommandBuffer> command_buffer = [gpu_helper_ commandBuffer]; id<MTLCommandBuffer> command_buffer = [gpu_helper_ commandBuffer];
command_buffer.label = @"TfLiteInferenceBPHWC4Convert"; command_buffer.label = @"TfLiteInferenceBPHWC4Convert";
id<MTLComputeCommandEncoder> convert_command = id<MTLComputeCommandEncoder> convert_command =
[command_buffer computeCommandEncoder]; [command_buffer computeCommandEncoder];
for (int i = 0; i < gpu_data_out_.size(); ++i) { for (int i = 0; i < gpu_data_out_.size(); ++i) {
output_tensors->at(i) = // Allocate output tensor.
output_tensors_gpu->at(i) =
[device newBufferWithLength:gpu_data_out_[i]->elements * sizeof(float) [device newBufferWithLength:gpu_data_out_[i]->elements * sizeof(float)
options:MTLResourceStorageModeShared]; options:MTLResourceStorageModeShared];
// Reshape tensor. // Reshape tensor.
[converter_from_BPHWC4_ convertWithEncoder:convert_command [converter_from_BPHWC4_ convertWithEncoder:convert_command
shape:gpu_data_out_[i]->shape shape:gpu_data_out_[i]->shape
sourceBuffer:gpu_data_out_[i]->buffer sourceBuffer:gpu_data_out_[i]->buffer
convertedBuffer:output_tensors->at(i)]; convertedBuffer:output_tensors_gpu->at(i)];
} }
[convert_command endEncoding]; [convert_command endEncoding];
[command_buffer commit]; [command_buffer commit];
cc->Outputs() cc->Outputs()
.Tag(kTensorsGpuTag) .Tag(kTensorsGpuTag)
.Add(output_tensors.release(), cc->InputTimestamp()); .Add(output_tensors_gpu.release(), cc->InputTimestamp());
#else #else
RET_CHECK_FAIL() << "GPU processing not enabled."; RET_CHECK_FAIL() << "GPU processing not enabled.";
#endif // !MEDIAPIPE_DISABLE_GPU #endif // !MEDIAPIPE_DISABLE_GPU
} else { } else {
// Output result tensors (CPU). // Output result tensors (CPU).
const auto& tensor_indexes = interpreter_->outputs(); const auto& tensor_indexes = interpreter_->outputs();
auto output_tensors = absl::make_unique<std::vector<TfLiteTensor>>();
for (int i = 0; i < tensor_indexes.size(); ++i) { for (int i = 0; i < tensor_indexes.size(); ++i) {
TfLiteTensor* tensor = interpreter_->tensor(tensor_indexes[i]); TfLiteTensor* tensor = interpreter_->tensor(tensor_indexes[i]);
output_tensors->emplace_back(*tensor); output_tensors_cpu->emplace_back(*tensor);
} }
cc->Outputs() cc->Outputs()
.Tag(kTensorsTag) .Tag(kTensorsTag)
.Add(output_tensors.release(), cc->InputTimestamp()); .Add(output_tensors_cpu.release(), cc->InputTimestamp());
} }
return ::mediapipe::OkStatus(); return ::mediapipe::OkStatus();
@ -640,6 +622,26 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
// Calculator Auxiliary Section // Calculator Auxiliary Section
::mediapipe::Status TfLiteInferenceCalculator::InitTFLiteGPURunner() {
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// Create and bind OpenGL buffers for outputs.
// These buffers are created onve and later their ids are jut passed to the
// calculator outputs.
gpu_data_out_.resize(tflite_gpu_runner_->outputs_size());
for (int i = 0; i < tflite_gpu_runner_->outputs_size(); ++i) {
gpu_data_out_[i] = absl::make_unique<GPUData>();
ASSIGN_OR_RETURN(gpu_data_out_[i]->elements,
tflite_gpu_runner_->GetOutputElements(i));
// Create and bind input buffer.
RET_CHECK_CALL(::tflite::gpu::gl::CreateReadWriteShaderStorageBuffer<float>(
gpu_data_out_[i]->elements, &gpu_data_out_[i]->buffer));
}
RET_CHECK_CALL(tflite_gpu_runner_->Build());
#endif
return ::mediapipe::OkStatus();
}
::mediapipe::Status TfLiteInferenceCalculator::LoadModel( ::mediapipe::Status TfLiteInferenceCalculator::LoadModel(
CalculatorContext* cc) { CalculatorContext* cc) {
ASSIGN_OR_RETURN(model_packet_, GetModelAsPacket(*cc)); ASSIGN_OR_RETURN(model_packet_, GetModelAsPacket(*cc));

2
mediapipe/docs/face_detection_desktop.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Face Detection](https://google.github.io/mediapipe/solutions/face_detection) [MediaPipe Face Detection](https://google.github.io/mediapipe/solutions/face_detection)

2
mediapipe/docs/face_detection_mobile_gpu.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Face Detection](https://google.github.io/mediapipe/solutions/face_detection) [MediaPipe Face Detection](https://google.github.io/mediapipe/solutions/face_detection)

2
mediapipe/docs/hair_segmentation_mobile_gpu.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) [MediaPipe Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation)

2
mediapipe/docs/hand_tracking_desktop.md
View File

@ -1 +1 @@
Content moved to [MediapPipe Hand](https://google.github.io/mediapipe/solutions/hand) Content moved to [MediaPipe Hands](https://google.github.io/mediapipe/solutions/hands)

2
mediapipe/docs/hand_tracking_mobile_gpu.md
View File

@ -1 +1 @@
Content moved to [MediapPipe Hand](https://google.github.io/mediapipe/solutions/hand) Content moved to [MediaPipe Hands](https://google.github.io/mediapipe/solutions/hands)

2
mediapipe/docs/multi_hand_tracking_mobile_gpu.md
View File

@ -1 +1 @@
Content moved to [MediapPipe Hand](https://google.github.io/mediapipe/solutions/hand) Content moved to [MediaPipe Hands](https://google.github.io/mediapipe/solutions/hands)

2
mediapipe/docs/object_detection_desktop.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Object Detection](https://google.github.io/mediapipe/solutions/object_detection) [MediaPipe Object Detection](https://google.github.io/mediapipe/solutions/object_detection)

2
mediapipe/docs/object_detection_mobile_gpu.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Object Detection](https://google.github.io/mediapipe/solutions/object_detection) [MediaPipe Object Detection](https://google.github.io/mediapipe/solutions/object_detection)

2
mediapipe/docs/object_tracking_mobile_gpu.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) [MediaPipe Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking)

2
mediapipe/docs/objectron_mobile_gpu.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe Objectron](https://google.github.io/mediapipe/solutions/objectron) [MediaPipe Objectron](https://google.github.io/mediapipe/solutions/objectron)

2
mediapipe/docs/template_matching_mobile_cpu.md
View File

@ -1,2 +1,2 @@
Content moved to Content moved to
[MediapPipe KNIFT](https://google.github.io/mediapipe/solutions/knift) [MediaPipe KNIFT](https://google.github.io/mediapipe/solutions/knift)

8
mediapipe/framework/calculator_base_test.cc
View File

@ -158,11 +158,11 @@ TEST(CalculatorTest, SourceProcessOrder) {
// Tests registration of a calculator within a namespace. // Tests registration of a calculator within a namespace.
// DeadEndCalculator is registered in namespace "mediapipe::test_ns". // DeadEndCalculator is registered in namespace "mediapipe::test_ns".
TEST(CalculatorTest, CreateByName) { TEST(CalculatorTest, CreateByName) {
MP_EXPECT_OK(CalculatorBaseRegistry::CreateByName( // MP_EXPECT_OK(CalculatorBaseRegistry::CreateByNameInNamespace( //
"mediapipe.test_ns.DeadEndCalculator")); "", "mediapipe.test_ns.DeadEndCalculator"));
MP_EXPECT_OK(CalculatorBaseRegistry::CreateByName( // MP_EXPECT_OK(CalculatorBaseRegistry::CreateByNameInNamespace( //
".mediapipe.test_ns.DeadEndCalculator")); "", ".mediapipe.test_ns.DeadEndCalculator"));
MP_EXPECT_OK(CalculatorBaseRegistry::CreateByNameInNamespace( // MP_EXPECT_OK(CalculatorBaseRegistry::CreateByNameInNamespace( //
"alpha", ".mediapipe.test_ns.DeadEndCalculator")); "alpha", ".mediapipe.test_ns.DeadEndCalculator"));

12
mediapipe/framework/deps/registration.h
View File

@ -322,16 +322,20 @@ class GlobalFactoryRegistry {
return functions()->Register(name, std::move(func)); return functions()->Register(name, std::move(func));
} }
// Same as CreateByNameInNamespace but without a namespace. // Invokes the specified factory function and returns the result.
// If using namespaces with this registry, the variant with a namespace
// argument should be used.
template <typename... Args2> template <typename... Args2>
static typename Functions::ReturnType CreateByName(const std::string& name, static typename Functions::ReturnType CreateByName(const std::string& name,
Args2&&... args) { Args2&&... args) {
return CreateByNameInNamespace("", name, std::forward<Args2>(args)...); return functions()->Invoke(name, std::forward<Args2>(args)...);
} }
// Same as IsRegistered(ns, name) but without a namespace. // Returns true if the specified factory function is available.
// If using namespaces with this registry, the variant with a namespace
// argument should be used.
static bool IsRegistered(const std::string& name) { static bool IsRegistered(const std::string& name) {
return functions()->IsRegistered("", name); return functions()->IsRegistered(name);
} }
static std::unordered_set<std::string> GetRegisteredNames() { static std::unordered_set<std::string> GetRegisteredNames() {

2
mediapipe/gpu/gl_quad_renderer.cc
View File

@ -169,7 +169,7 @@ void QuadRenderer::GlTeardown() {
glEnableVertexAttribArray(ATTRIB_VERTEX); glEnableVertexAttribArray(ATTRIB_VERTEX);
glBindBuffer(GL_ARRAY_BUFFER, vbo_[0]); glBindBuffer(GL_ARRAY_BUFFER, vbo_[0]);
glBufferData(GL_ARRAY_BUFFER, sizeof(mediapipe::kBasicSquareVertices), glBufferData(GL_ARRAY_BUFFER, sizeof(mediapipe::kBasicSquareVertices),
mediapipe::kBasicSquareVertices, GL_STATIC_DRAW); vertices, GL_STATIC_DRAW);
glVertexAttribPointer(ATTRIB_VERTEX, 2, GL_FLOAT, 0, 0, nullptr); glVertexAttribPointer(ATTRIB_VERTEX, 2, GL_FLOAT, 0, 0, nullptr);
glEnableVertexAttribArray(ATTRIB_TEXTURE_POSITION); glEnableVertexAttribArray(ATTRIB_TEXTURE_POSITION);

2
mediapipe/java/com/google/mediapipe/components/PermissionHelper.java
View File

@ -18,8 +18,8 @@ import android.Manifest;
import android.app.Activity; import android.app.Activity;
import android.content.pm.PackageManager; import android.content.pm.PackageManager;
import androidx.core.app.ActivityCompat; import androidx.core.app.ActivityCompat;
import androidx.core.content.ContextCompat;
import android.util.Log; import android.util.Log;
import androidx.core.content.ContextCompat;
/** Manages camera permission request and handling. */ /** Manages camera permission request and handling. */
public class PermissionHelper { public class PermissionHelper {